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Peer-reviewedCC BYNature Communications

Fast, flexible analysis of differences in cellular composition with crumblr

  • Gabriel E. Hoffman1,2,3,4,5,6
  • Panos Roussos7,8,9,10,11,12
12 affiliations
  1. 1Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  2. 2Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA. [email protected]
  3. 3Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  4. 4Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  5. 5Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  6. 6Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, NY, USA. [email protected]
  7. 7Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  8. 8Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA. [email protected]
  9. 9Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  10. 10Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  11. 11Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  12. 12Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, NY, USA. [email protected]
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Abstract

Changes in cell type composition play an important role in human health and disease. Recent advances in single-cell technology have enabled the measurement of cell type composition at increasing cell lineage resolution across large cohorts of individuals. Yet this raises new challenges for statistical analysis of these compositional data to identify changes in cell type frequency. We introduce crumblr (DiseaseNeurogenomics.github.io/crumblr), a scalable statistical method for analyzing count ratio data using precision-weighted linear mixed models incorporating random effects for complex study designs. Uniquely, crumblr performs statistical testing at multiple levels of the cell lineage hierarchy using a multivariate approach to increase power over tests of one cell type. In simulations, crumblr increases power compared to existing methods while controlling the false positive rate. We demonstrate the application of crumblr to published single-cell RNA-seq datasets for aging, tuberculosis infection in T cells, bone metastases from prostate cancer, and SARS-CoV-2 infection.

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Cite

BibTeXRIS
BibTeX
@article{hoffman2026fast,
  title = {{Fast, flexible analysis of differences in cellular composition with crumblr}},
  author = {Gabriel E. Hoffman and Panos Roussos},
  journal = {Nature Communications},
  year = {2026},
  month = sep,
  doi = {10.1038/s41467-026-75681-7},
  url = {https://doi.org/10.1038/s41467-026-75681-7},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Fast, flexible analysis of differences in cellular composition with crumblr
AU  - Gabriel E. Hoffman
AU  - Panos Roussos
PY  - 2026
DA  - 2026/09/23
JO  - Nature Communications
DO  - 10.1038/s41467-026-75681-7
UR  - https://doi.org/10.1038/s41467-026-75681-7
AB  - Changes in cell type composition play an important role in human health and disease. Recent advances in single-cell technology have enabled the measurement of cell type composition at increasing cell lineage resolution across large cohorts of individuals. Yet this raises new challenges for statistical analysis of these compositional data to identify changes in cell type frequency. We introduce crumblr (DiseaseNeurogenomics.github.io/crumblr), a scalable statistical method for analyzing count ratio data using precision-weighted linear mixed models incorporating random effects for complex study designs. Uniquely, crumblr performs statistical testing at multiple levels of the cell lineage hierarchy using a multivariate approach to increase power over tests of one cell type. In simulations, crumblr increases power compared to existing methods while controlling the false positive rate. We demonstrate the application of crumblr to published single-cell RNA-seq datasets for aging, tuberculosis infection in T cells, bone metastases from prostate cancer, and SARS-CoV-2 infection.
SN  - 2041-1723
ER  -